Molecular subtypes have been recently derived for various types of cancer, in an attempt to characterize the inter-tumoral heterogeneity. In this work we explore the possibility of constructing predictors for molecular subtypes based on histopathology images. For this, we introduce a novel 2-level bag-of-features method and we apply it to a collection of colorectal cancer samples. The resulting image features capture some relevant tumor morphology patterns and led to a classifier performing similarly to one constructed from features annotated by an expert pathologist. The significance of our results extends beyond subtype prediction since they demonstrate a possible approach to multimodal (histopathology and molecular) data mining and biomarker identification.