Small multiples are a popular method of summarizing and comparing multiple facets of complex data sets. Since they typically do not take into account correlations between items, serial inspection is needed to search and compare items, which can be ineffective. To address this, we introduce CorrelatedMultiples, an alternative of small multiples in which items are placed so that distances reflect dissimilarities. We propose a constrained multidimensional scaling (CMDS) solver that preserves spatial proximity while forcing items to fit within a fixed region. We evaluate the effectiveness of CorrelatedMultiples through a controlled user study, and compare the CMDS method with competing methods. We also demonstrate the usefulness of CorrelatedMultiples in a case study on visual analysis of stock market trends.