Glioblastoma (GBM) is an aggressive brain tumor with median patient survival of about 15 months. The key reason for our poor understanding of GBM is that it is a highly heterogeneous disease with molecular heterogeneity and spatiotemporal heterogeneity. Radiogenomics is a rapidly emerging field that seeks to develop non-invasive imaging signatures associated with genetic mutations of such cancers from magnetic resonance imaging (MRI) scans. This paper develops a technique to quantify the molecular heterogeneity of GBM using radiogenomic features. We fit a probabilistic model that predicts the likelihood of 13 different genetic mutations and use a technique called Intensive Principal Component Analysis (InPCA) to visualize the predictions of this model. The principal components of InPCA form an interpretable coordinate system for characterizing the imaging signatures of different GBM pathways; this coordinate system is consistent with clinical research. We quantify the overlap of different pathway groups to characterize the molecular heterogeneity of GBM. Such analysis can potentially be used in the future for targeted treatments, e.g., when patients present with one or more of these overlapping pathways.