Understanding clusters after applying PCA then K-means
PCA removes the connection with the original features,so the interpretation of the visualisations in the principle component space is therefore not very meaningful.
E.g. cluster A has higher values of PC1, where cluster B has higher values of PC2.
If you can clearly see that PC1 is only representative of Feature X, then fine, but this isn't often the case.
Instead use PCA to discover which features best represent the data, and use this to remove unnecessary (unrepresentative) features so that you can visualise the original data using its most important original features. And therefore describe the clusters with meaningful differences.
You might want to experiment with a scatter matrix, to explore which features spaces have the cleanest distinction between clusters.