Compare multiple values from a DataFrame against a single row from another

I'm trying to compare address values for inaccuracies, for example, given multiple records like:

I've a dataframe where I store all the "main" addresses by checking if the Apartment column is empty like so:

main_address = df['Apartment'].isnull()

df_st = pd.DataFrame({'Reference':df[main_address].Reference, 'Address':df[main_address].Address, 'PostCode':df[main_address].PostCode})

df_st will look like this:

df has over 1K records, but df_st containing the "main" address ends up with approx. 200 records.

I'm trying to create a new DataFrame where I can identify where the records don't match by domparing df against df_st.

THE PROBLEM

I try the below:

# Clean the Reference values
refs_list = df['Reference'].str.split('/').str[0]
df['Reference'] = refs_list

# Create a new column titled issues and flag if the references match
df['issues'] = np.where(df['Reference'] == df_st['Reference'], 'True', 'False')

I want the above for the Address and PostCode unfortunately this does not work since df and df_st don't have the same shape.

I'm struggling to find a way to achieve a comparison between the two DataFrames df against df_st.

I want to compare all matching Reference row values from df against it's matching from df_st and if one of them don't match create a new column title Issues and store the conflicting column there.

MY DESIRED OUTCOME

Given the data above, after comparing df data against df_st results in a new DataFrame, like below

Where Address appears as an issue in the column Issues since the address don't match against df_st, same for PostCode since it differs from df_st

IN A NUT SHELL

All I want to to know how to compare matching rows by Reference from a DataFrame against another and compare the other values Address and PostCode.

Hope that makes sense.