The coordination of the synthesis of functionally-related proteins can be achieved at the post-transcriptional level by the action of common regulatory molecules, such as RNA-binding proteins (RBPs). Despite advances in the genome-wide identification of RBPs and their binding transcripts, the protein-RNA interaction space is still largely unexplored, thus hindering a broader understanding of the extent of the post-transcriptional regulation of related coding RNAs. Here, we propose a computational approach that combines protein-mRNA interaction networks and statistical analyses to provide an inferred regulatory landscape for more than 800 human RBPs and identify the cellular processes that can be regulated at the post-transcriptional level. We show that 10% of the tested sets of functionally-related mRNAs can be post-transcriptionally regulated. Moreover, we propose a classification of (i) the RBPs and (ii) the functionally-related mRNAs, based on their distinct behaviors in the functional landscape, hinting towards mechanistic regulatory hypotheses. In addition, we demonstrate the usefulness of the inferred functional landscape to investigate the cellular role of both well-characterized and novel RBPs in the context of human diseases.