Schizophrenia is currently diagnosed by physicians through evaluation of their clinical assessment and a patient's self-reported experience over the longitudinal course of the illness. There is great interest in identifying biologically based markers at illness onset, rather than relying on the evolution of symptoms across time. Functional connectivity shows promise in providing individual subject predictive power. However, the majority of previous studies considered only the analysis of functional connectivity during resting-state or performance of a single task. Changes in connectivity between rest and multiple tasks have not been used in the discrimination of schizophrenia patients from healthy controls. In this work, we propose a framework for classification of schizophrenia patients and healthy control subjects based on functional network component pairs which show consistency between patients and controls across levels of the resting-state data and task hierarchy. Our results show that these functional network components as a function of task contain valuable information for individual prediction of schizophrenia patients. Such information is useful for training and replicates in testing. Performance was improved significantly (up to ∼20%) relative to a single FNC (resting-state) measure.